In recent years, with the increasing seriousness of traffic jam and human attention to time value, the distribution of path travel time becomes one of the most concerned problems of travelers. At present, travel time estimation based on taxi data is used most widely because taxi have the advantages of wide coverage range, high timeliness, low cost of data collection and the like compared with other data sources. Jenelius E proposes a statistical method for travel time estimation for urban road networks using vehicle running tracks observed by low frequency GPS floating car in “Travel time estimation for urban road networks using low frequency probe vehicle data”. Road section turning features and travel conditions are used as explanatory variables to depict influence factors behind temporal and spatial distribution of speed variation, which is quite practical for transportation forecast. Chen compares a method for travel time estimation based on road sections and a method for travel time estimation based on paths respectively using floating car data in “Dynamic Freeway Travel Time Prediction Using ProbeVehicle Data: Link-based vs. Path-based”, then discusses the influence of the proportion of the floating car on estimation precision, proposes travel time estimation of road sections based on floating car data through kalman filtering, and conducts simulation verification.
With respect to such research methods, there are two problems currently: firstly, the distribution of travel time of each road section is simply superposed as distribution of path travel time, which may increase an error of estimation of the path travel time; secondly, driving behaviors of taxi drivers under two operation states of no passenger and passenger service may be different, so the difference of the driving behaviors inevitably result in a difference between a result of travel time estimation from direct use of taxi data and a true value. Based on this, the present invention proposes a method for more accurately estimating path travel time based on taxi data, and fully considers the influence of the operation states of the taxi on the travel time estimation of the path, thereby proposing a more accurate improvement method.